Currently, the creation of an ASR natural language application requires significant effort from subject matter experts and grammar developers primarily related to the mapping of potential spoken strings of words (utterances) to specific meanings (semantics). This mapping is referred to as the Tagging process. The Tagging process requires review of all possible combinations of utterances and assignment to a specific semantic upon which a natural language application can react. The sample size of potential utterances that must be tagged can be too large for a single subject matter expert or grammar developer (Taggers) to process. Limiting the Tagging process to a single Tagger can lead to excessive time consumed in the Tagging process and/or an insufficient mapping between potential utterances and their associated semantic.
Therefore, what is needed is collaborative solution that allows multiple subject matter experts and grammar developers to not only accelerate the Tagging process but to also to improve the accuracy of tagged utterances.